The present invention generally relates to vehicle crash discrimination systems, and more particularly to a method and system for reducing the effect of structural resonance on vehicle crash discrimination system performance.
Typically, vehicle crash discrimination systems function to identify the nature of a vehicle collision. In essence, a vehicle crash discrimination system attempts to discriminate between minor impacts and vehicle decelerations of a magnitude sufficient to warrant deployment of a passenger restraint device, such as an airbag. In the past, vehicle crash discrimination systems have utilized relatively small sets of actual vehicle crash waveform data stored within a resident memory. The actual crash waveform data is generated by crashing test vehicles and recording the appropriate data, with each waveform set representing a different type of crash scenario. Such crash discrimination systems operate by comparing the stored sets of crash waveforms with vehicle acceleration data.
However, either in a collision or during the operation of the vehicle, resonance generated by a vehicle's frame, body, and other structural elements can distort the measured acceleration data. The resonance generated by different makes and models of vehicles is primarily unique to each particular vehicle because of the inherently different vehicle structural elements. Thus, crash waveform data sets which accommodate structural resonance must be created for each vehicle make and model by crashing vehicles of the same make or model under different crash conditions (i.e., vehicle speed, crash location). Because known vehicle crash discrimination systems have failed to recognize and reduce the resonant component present in the measured acceleration data, it heretofore has been impossible to use a generic set of vehicle crash waveforms in the crash discrimination system of a vehicle irrespective of the vehicle make and model.
Further, because crash waveforms have heretofore been produced by actually crashing vehicles, only small sets of crash waveform data were generated due to the high cost of crash vehicles. The small sets of crash waveform data have been generally used to represent all possible crash situations in the development and calibration of a crash discrimination system for a particular vehicle make or model. However, small finite sets of crash waveforms do not provide a reliable or realistic representation of all crash scenarios which can occur in real world situations.
Thus, known crash discrimination systems which have been designed or calibrated to use unique finite sets of crash waveforms are inherently limited with respect to reliability over the entire range of possible crash scenarios, have required expensive testing and calibration procedures to develop the unique set of crash waveforms, and have not been portable between vehicle makes and models.